Empirical software engineering research often depends on datasets of code repository artifacts, where sampling strategies are employed to enable large-scale analyses. The design and evaluation of these strategies are critical, as they directly influence the generalizability of research findings. However, sampling remains an underestimated aspect in software engineering research: we identify two main challenges related to (1) the design and representativeness of sampling approaches, and (2) the ability to reason about the implications of sampling decisions on generalizability. To address these challenges, we propose a Domain-Specific Language (DSL) to explicitly describe complex sampling strategies through composable sampling operators. This formalism supports both the specification and the reasoning about the generalizability of results based on the applied sampling strategies. We implement the DSL as a Python-based fluent API, and demonstrate how it facilitates representativeness reasoning using statistical indicators extracted from sampling workflows. We validate our approach through a case study of MSR papers involving code repository sampling. Our results show that the DSL can model the sampling strategies reported in recent literature.

翻译：实证软件工程研究通常依赖于代码仓库制品的数据集，其中采用采样策略以实现大规模分析。这些策略的设计与评估至关重要，因为它们直接影响研究结果的可推广性。然而，采样在软件工程研究中仍是一个被低估的方面：我们识别出两个主要挑战，涉及（1）采样方法的设计与代表性，以及（2）推理采样决策对可推广性影响的能力。为应对这些挑战，我们提出一种领域特定语言（DSL），通过可组合的采样算子显式描述复杂的采样策略。该形式化方法支持基于所应用采样策略的结果可推广性的规范与推理。我们将该DSL实现为基于Python的流式API，并演示如何利用从采样工作流中提取的统计指标来促进代表性推理。我们通过一项涉及代码仓库采样的MSR论文案例研究验证了该方法。结果表明，该DSL能够对近期文献中报告的采样策略进行建模。